Data Quality and Governance Practitioner Model Job Description

Role purpose

This evidence-derived model describes an operational data quality and data governance role in a United States organization. It is a reusable job-description template for local adaptation. It is not a live vacancy, a promise of employment, or a universal employer policy.

The practitioner helps an authorized business team use a bounded dataset with clear meaning, accountable ownership, testable quality expectations, traceable movement, and a reviewable way to resolve problems. The work starts with a business decision or service need. It ends with evidence that named people can understand the data, its limitations, its quality status, and the actions required when a rule fails. The local employer sets the reporting line, domain, systems, authority, access permissions, and required experience.

Position summary for reuse

The Data Quality and Governance Practitioner works with data owners, stewards, engineers, analysts, and data consumers to keep priority data assets fit for approved uses. The role documents critical data and business definitions; helps establish quality rules and monitoring; maintains metadata and lineage; records and follows up on data issues; and supports governance decisions with concise evidence. The practitioner tests and recommends within delegated authority, while the organization’s named owners and specialist functions approve policy, access, privacy, security, legal, and risk decisions.

Core responsibilities

Scope, ownership, and business meaning

• Start each assignment by identifying the consuming decision or process, the bounded dataset, its critical data elements, its source, and the person accountable for its business meaning.
• Maintain an owner and steward register with the domain, responsibility, contact route, decision forum, and date of the last confirmed assignment. Bring an unclear or disputed assignment to the designated governance lead or owner forum.
• Work with business specialists to define important terms and approved uses in plain language. Record conflicting definitions and the decision that resolves them rather than silently choosing one interpretation.
• Help data consumers understand what an asset covers, when it was updated, what is excluded, and which questions require the owner’s judgment.

Quality rules and measurement

• Profile and reconcile authorized data samples to find missing, inconsistent, duplicated, out-of-range, or unexpectedly changed values. Record the population, time period, method, and known coverage gaps.
• Translate a business fitness need into a quality rule that states the field or population, test logic, threshold, run frequency, evidence retained, accountable owner, and response to a failure.
• Review rule results with the people who know the process. Distinguish a true defect from an accepted exception, a rule-design problem, or an expected business change.
• Maintain a small quality scorecard that shows the condition of priority data, open exceptions, trend direction, and decisions needed. Avoid presenting one composite score without its component measures and limitations.

Metadata, lineage, and change

• Maintain useful business and technical metadata in the approved glossary, dictionary, or catalog: definition, owner, steward, source, update timing, criticality, permitted use, and known limitations.
• Document source-to-consumer lineage for priority elements, including important transformations, handoffs, and downstream reports or products. Mark unverified links as gaps.
• Assess the effect of a changed definition, source, rule, or system on consumers and controls. Update the relevant metadata and lineage records after the authorized change is implemented.
• Support master or reference data work where it belongs to the local domain: identify authoritative records, validate change requests, and record the approval path. Do not assume that this role owns every master-data process.

Issues, decisions, and adoption

• Receive and log a data issue with the affected asset, evidence, business impact, severity proposal, owner, next action, and target review date. Keep sensitive source records out of broadly shared logs.
• Investigate likely causes with the relevant business and technical teams. Coordinate correction, record the decision, and retest the affected rule or output before marking the issue resolved.
• Escalate unresolved ownership, repeated failures, material business impact, policy conflict, access concern, or a proposed risk acceptance through the employer’s defined route.
• Prepare concise decision notes for a steward review or governance forum: issue, evidence, options, recommendation, approver, decision, action owner, and follow-up date.
• Explain standards and changes to data creators and consumers, answer practical questions, and follow up to see whether the agreed process is being used.
• Where approved, use automation or AI-assisted tools to draft documentation, suggest candidate rules, summarize issue evidence, or propose lineage links. Verify output against source systems and a named human owner before it becomes an accepted record.

Expected work products

A local employer may combine these products in one approved system. The test is whether the information is complete, current, understandable, and usable in a real decision.

Capabilities

Core capabilities for an operational practitioner

• Explain data quality in terms of a specific business use, not only a generic cleanliness score.
• Profile and reconcile authorized data with SQL-style queries, controlled extracts, or equivalent approved methods; check the population and logic before drawing a conclusion.
• Write an unambiguous quality rule and explain its threshold, exceptions, evidence, and response.
• Document business definitions, metadata, and lineage so that another practitioner can maintain them.
• Triage an issue, distinguish observation from hypothesis, coordinate a root-cause investigation, and verify a correction.
• Facilitate a short discussion between business and technical colleagues, record decisions, and follow up on agreed actions.
• Apply the employer’s approved data-handling rules and know when privacy, security, legal, risk, or access specialists must decide.

Role-dependent or preferred capabilities

• Experience with master and reference data lifecycles, matching, survivorship, and hierarchy changes.
• Experience configuring a data catalog, lineage view, quality-monitoring tool, or issue workflow.
• Familiarity with a particular industry’s data definitions and control obligations.
• Ability to lead a cross-domain governance forum, design an operating model, or coach other stewards.
• Experience with approved automation that assists documentation or monitoring while preserving human review.

These capabilities are not a universal checklist. The hiring organization should separate genuinely required skills from preferred or learnable skills for its own level and domain.

Tools and systems

Use the employer-approved combination of a query or profiling environment, spreadsheet or controlled extract, glossary or catalog, lineage view, quality-monitoring dashboard, and issue tracker. The role may also work with a master-data or ERP workflow and with AI-assisted documentation tools. The job description should name a vendor product only when that product is actually part of the local work and distinguish required hands-on proficiency from general familiarity. Tool output is supporting evidence; it does not replace business ownership or approval.

Experience and level

This model can be calibrated to an entry or early-career analyst, an established data steward or specialist, or a senior lead. At an early-career level, work should be bounded to an assigned domain and reviewed by an experienced owner or lead. An established practitioner can independently coordinate rules, documentation, and issues within delegated authority. A senior lead may design standards, convene forums, resolve cross-domain conflicts, and coach stewards, subject to the organization’s policy. The employer should set any education, years of experience, certification, or domain requirements from the actual responsibilities. No single credential or vendor certification is assumed.

Interfaces, authority, and escalation

The practitioner works most often with business data owners and stewards, data creators, engineering and platform teams, analytics and reporting teams, and data consumers. Security, privacy, legal, compliance, risk, and audit colleagues join when the matter falls within their specialist responsibility. A governance lead or council resolves cross-domain questions under the employer’s approved structure.

Within delegated authority, the practitioner may document definitions, profile authorized samples, propose rules, log issues, coordinate investigation, prepare a recommendation, and verify a retest. A named owner or authorized forum approves business definitions, rule thresholds, priorities, exceptions, certification, and risk acceptance when local policy assigns those decisions to them. Access, privacy, security, legal, and regulated decisions stay with the authorized specialist or decision maker.

Escalate when no accountable owner can be identified; two domains disagree on a definition; a quality failure materially affects a decision, service, or obligation; an issue recurs after correction; lineage is too incomplete for an impact decision; a proposed workaround would weaken an approved control; or an access or sensitive-data concern appears. Record the evidence and the decision needed, then follow the local escalation route.

Working rhythm

• Daily or event-driven: review priority quality alerts and intake; confirm owners; triage material issues; update the issue log and communicate next actions.
• Weekly or other locally agreed cycle: review open issues with stewards, inspect rule trends, reconcile exceptions, and prepare owner decisions.
• Monthly or other governance cycle: refresh the scorecard, review definitions and rule changes, follow up on overdue actions, and update the decision record.
• On a source, product, policy, or process change: assess lineage and consumers, revise metadata and tests, obtain required approvals, and monitor the first run after release.

These frequencies are examples of a workable rhythm. The employer sets the actual cadence, service levels, and escalation times.